(Received 9 June 1997; in revised form 29 August 1997; accepted 29 August 1997)

Journal of Oceanography, Vol. 53, pp. 623 to 631. 1997 Trends and Interannual Variability of Surface Layer Temperature in the Indian Sector of the Southern Ocean Observed by Japanese Antarctic Research Expeditions SHIGERU AOKI National Institute of Polar Research, 1-9-10, Kaga, Itabashi, Tokyo 173, Japan (Received 9 June 1997; in revised form 29 August 1997; accepted 29 August 1997) Interannual temperature variation of surface layer (0 400 m) is examined from 12 years (1983 1995) of XBT data obtained by Japanese Antarctic Research Expeditions in the Indian sector of the Southern Ocean. To extract interannual variation, the effect of positional differences in data sampling points is corrected by using the World Ocean Atlas 1994 climatological field. Statistical analysis is made to verify the reliability of the correction qualitatively, and the ambiguity of the obtained signal is discussed. Significant warming trends of 0.02 0.04 C/year are observed at depths of 200 400 m in the western region (50 90 E, 58 61 S) from both the observed and positionally corrected time series. Besides these trends, variations of 4 5 year cycles are suggested. The fluctuations at most of the levels except 50 150 m show similar patterns to that at the surface. The effects of different dynamics are suggested in the Winter Water layer. Keywords: The Southern Ocean, Japanese Antarctic Research Expeditions, surface layer temperature, warming trends, 4 5 year cycles variations. 1. Introduction Oceans in high latitudes have an important role in determining the global climate system. The ocean surface, where intense interactions occur between atmosphere and ocean or ocean and ice, is fundamental in giving an initial and boundary condition to the deep ocean and overlying atmosphere. The Southern Ocean is a unique ocean which connects the world oceans, and the Antarctic Circumpolar Current (ACC) plays an important role in the global transport of mass, heat, and momentum. Various research has been conducted pertaining to water mass characteristics, and basic characteristics of water mass are well described. The properties of water near the surface vary considerably for a given depth in the Southern Ocean. Winter Water lays just beneath the surface and exhibits a very low temperature which is near to freezing point. Below this water lays Circumpolar Deep Water, which is characterized as warmer and more saline. The spatial distribution of these waters and the existence of associated frontal structures have been extensively studied (Gordon and Molinelli, 1982; Deacon, 1982; Orsi et al., 1995). Recently reanalysis of historical data has shown longterm variations in this region. A multi-decadal warming trend in sea surface temperature (SST) since the 1970s is observed in the Indian Ocean and Southern Ocean (Ort et al., 1987; Smith et al., 1994). Bindoff and Church (1992) suggested warming of the water column in the southwest Pacific Ocean along 43 S in multi-decadal time scale. However, actually, the observations are very sparsely and irregularly distributed in this region. The latter observation consists of only two one-time surveys apart by 22 years, therefore the result may include ambiguities such as an aliasing of other signals. In recent years, an interannual variation whose cycle is 4 5 years has been discovered by analyses of satellite data covering the ocean and assimilation data of the atmosphere. White and Peterson (1996) and Jacobs and Mitchell (1996) describe the characteristics of the 4 5 year periods eastward propagating feature (the Antarctic Circumpolar Wave) from SST and sea surface height (SSH) data. The SSH is reported to have almost the same variation with the SST, but the actual vertical structure of the variation has not been entirely understood. In the Indian sector of the Southern Ocean, oceanographic observations have been conducted by the Maritime Safety Agency under the auspices of the Japanese Antarctic Research Expedition (JARE). The observations include measurements of the surface layer temperature in nearly the same region every year. The data can provide useful information in analyzing interannual variations in this datalacking region. To investigate interannual variations of the vertical temperature structure, we examined the JARE expendable bathythermographs (XBT) data. The observations have been done at slightly different positions, so the effect of the different sampling in space must be excluded to study real interannual oceanic signal. The distribution and treat- Copyright The Oceanographic Society of Japan. 623

ment of the data are described in Section 2. Reliability of the sampling correction are examined, and the time series obtained are studied in Section 3. 2. Data Sources The JARE XBT data are used to study interannual variations of the surface and subsurface temperature field. Oceanographic observations have been conducted on board the icebreaker Shirase along nearly the same track every year (Fig. 1). Shirase starts from Fremantle in November, proceeds southward along the 110 E line, and reaches Syowa station (40 E, 69 S) in December. From JARE-29 (1987/1988), she departured from Syowa to the east, and returned northward to Sydney along the 150 E line in the next March. The data from JARE-25 to JARE-36 are collected; the data from JARE-25 to JARE-33 (1983 1992) are transcribed from JARE Data Reports, Oceanography (Iwanami and Futatsumachi, 1986; Iwanami and Tohju, 1987; Iwanaga and Thoju, 1987; Michida and Inazumi, 1988; Ito and Ishii, 1989; Ikeda and Matsumoto, 1991; Ikeda and Kojima, 1992; Nakamura and Noguchi, 1993; Tanaka and Noguchi, 1995), and the data from JARE-34 to JARE- 36 (1992 1995) are provided by the Japan Oceanographic Data Center/Maritime Safety Agency. For detecting interannual signals of actual oceanic variations, various components are considered as erroneous: mesoscale variation such as meanders and eddies of the ACC, seasonal cycle signals, and contamination caused by the difference in sampling points. To avoid these major errors, data were selected as follows. The seasonal signal, which is intense especially above the mixed-layer, can leak into the interannual signal if the sampling seasons are different to each other. Therefore data are selected to be taken at almost the same time in each year. Then the points from zonal tracks are extracted. The vertical profiles of the mean temperature are horizontally more homogeneous in zonal direction than in meridional direction. Moreover, in zonal sections, the ensemble average can reduce the effect of mesoscale variations, while the data from the meridional section are so sparsely sampled that it is difficult to eliminate the contamination by the intense meanders and eddies. Thus analysis domains are selected to the western region (50 90 E, 58 61 S) in December and the eastern region (90 130 E, 60 65 S) in March. The observation points selected are shown in Fig. 2. The data are available in 9 13 December for 1983 94 (12 years from JARE-25 to JARE- 36) and in 4 11 March for 1988 95 (8 years from JARE-29 to JARE-36), respectively. The number of data points is 6 14 (average 8.5) in December and 9 36 (average 18.5) in March. Data from 0 to 400 m are analyzed because of the sparseness of data below that depth. The positions for data sampling vary slightly from year to year. Data are densely and evenly located in the eastern region, but the track routes fluctuate meridionally year by Fig. 1. Distribution of the XBT observation points of JARE-25 to JARE-36. 624 S. Aoki

Fig. 2. Distribution of the selected points in each year. Triangles denote the points in the western region, and open circles denote those in the eastern region. Characters at the upper left denote the years for December and those at the upper right denote the years for March. year. The most northern line was taken along 61 S in 1990, and the most southern line along south of 64 S in 1988 and 1995. The temporal mean temperature is different by region, and therefore contamination through different sampling points can occur. The contamination can be eliminated by subtracting the temporal mean field during the observation period. However such a data set for the averaged field is not available in this region because of the lack of in situ observations. Instead, climatological temperature data are used as an approximate mean field to correct the effect of the change of the sampling points. Variations are calculated by subtracting the climatological component from the observed data. Then the whole data in a certain year are combined to reduce the mesoscale signal and to suppress the measurement error in XBT observations. The climatological temperature data of the World Ocean Atlas 1994 (hereafter abbreviated by WOA94, Boyer and Levitus, 1994) are used to correct the difference in the sampling points. The monthly averaged data for December and March are used as reference temperature fields. It can be thought that a sufficient number of data are used to derive the mean temperature field for the summer season in the analysis domain. For the data in December (in the western region), an estimate at a certain point is derived from about 45 observations in average at the surface and from about 27 observations at 400 m. Data in March (in the eastern region) are derived from about 56 observations at the surface and from about 25 observations at 400 m. For SST, the optimally interpolated SST data (hereafter abbreviated by OISST, Reynolds, 1988; Reynolds and Smith, 1994) are widely used to study the variability at the sea surface. The estimates are derived from in situ and satellite AVHRR data using an optimal interpolation method. The OISST data are given weekly on 1 1 grids over the whole ocean. The interannual variations of the SST of the XBT observations (JARE-SST) are compared with those of the OISST. 3. Results 3.1 Reliability of the sampling point correction Total observations for a certain year are combined into a spatially averaged form at every depth. The vertical temperature profiles in the western region (December) and eastern region (March) are shown in Figs. 3a and 3d, respectively. In the western region, the local temperature minimum from 1.5 to 1.0 C is located at depths of 50 100 m, which corresponds to the Winter Water layer. In the eastern region, the temperature minimum is located at depths of 75 100 m. The ranges of temperature variations at most Interannual Variability in the Southern Ocean 625

Fig. 3. Spatially averaged temperature profiles for each year. a) is for the observations, b) for the climatology (WOA94), and c) for the corrected variations by climatology for the western region. d), e), and f) are the same as a), b), and c) but for the eastern region. levels are larger in the eastern region than in the western region; the standard deviations are 0.2 0.4 C at 0 200 m and about 0.1 C at 250 400 m in the western region, while those in the eastern region are 0.25 0.8 C at 0 400 m. The corresponding WOA94 climatological profiles are shown in Figs. 3b and 3e. The variations of the climatology component are thought to represent the difference in positional sampling points, and it is referred to the positional factor hereafter. The profiles are similar in the western region. The magnitudes of the standard deviations are 2 3 times smaller than those of the original JARE observations. However, those in the eastern region are broadly distributed. This shows that the large change of the background mean temperature and the movement of the observation tracks are critical to the temperature observed in this region. To correct the error due to the different sampling points, temperature 626 S. Aoki

variations are estimated by subtracting the positional factor (WOA94 climatological temperature) from the observed temperature (Figs. 3c and 3f). The ranges of the corrected variations are much reduced in the eastern region. The time series of the observed temperature variations and positional factor components due to different stations in space are shown in Fig. 4, and those of the corrected variations (anomalies) are shown in Fig. 5. In the western region, the variations of the positional factor component are small compared with the observed variations, and the corrected variations are altered only slightly from the original ones. It can be seen in the eastern region that the variations of the positional factor are large and that the observations are strongly controlled by this positional factor component. The cold temperature anomalies observed in 1988, 1992, and 1995 correspond to the southern most positions (see Fig. 2). Note that the variation of the tracks exhibits cycles of about 4 5 years in duration. The variations after the correction are drastically altered from the original ones in the eastern region. The time series of the temperature variations before and after the sampling correction are statistically compared to verify whether the correction is properly made. The correlation between the observation and positional factor and that between the corrected variation and positional factor are examined to see how largely the time series are controlled by positional changes (Fig. 6). A significantly positive correlation between the original observation and positional factor component indicates that the variation is highly controlled by the positional difference, and a significantly negative correlation can be thought of as a sign of excessive correction. Note that if the real signal correlates with the positional factor (for example the observations are conducted at relatively northern stations in the years of warm anomalies), the correlation will off course be significant. For the western region, the correlation coefficient in the upper levels (0 150 m) is reduced after the correction; Fig. 4. Time series of the observed temperature variations (circles) and positional factor from spatial differences of stations (triangles) for 0 to 400 m. Time dependent components are plotted with 1 C offset. a) is for the western region and b) is for the eastern region. Fig. 5. As in Fig. 4, but for those of the corrected variations (anomalies). Interannual Variability in the Southern Ocean 627

Fig. 6. Correlation coefficients between the observations and positional factor, and between the corrected variations and positional factor. Squares (triangles) denote the correlation before (after) the sampling correction. Open (filled) marks represent those in the western (eastern) region. the significant correlations with the observations decrease to non-significant levels, so this is consistent with the validity of the correction. However, in depths from 250 to 400 m, no significant correlations are found in the observations, but negative correlations increase after the correction. The correlation coefficient at 250 m has a significant negative correlation over a 90% confidence level. At these depths the results may be more reliable in the case of no sampling correction. The correlation between the observation and positional factor is quite high through all depths in the eastern region, which means the observed temperature is highly controlled by the sampling positions. The correlations after the positional corrections decrease in the upper levels (0 250 m). However, in the levels of 300 400 m, the correlations are significantly negative. The variation in the meridional position of the ship tracks has approximately 4 year cycles, and the positional factor due to this variation seems to have a highly negative correlation with the real oceanic signal. The positional factor is large in this region, thus the reliability of the corrected variation is relatively low. 3.2 Trend and interannual variation of the temperature field From the time series of both the original observations and positionally corrected data, characteristics of long-term variations are analyzed. Fig. 7. Estimated linear trends in the western region. Solid circles are for the corrected variations, open circles for the observations and triangles for the positional factor. In the western region, warming trends of 0.02 0.05 C/ year are detected for all levels in both the observed and corrected time series (Fig. 7). Especially at 200 400 m, the trends are 0.02 0.04 C/year for both the observed and corrected variations (with a 97.5% F-test confidence level for the observation and a 99% confidence level for the corrected variation, this is significant). The trends derived from the positional factor components are 0.01 to 0.015 C/ year and are not so statistically significant. If we think of this range as an error criteria, the values from the observed and corrected variations are much larger than the error level. The trends derived in the eastern region are not statistically significant because of the large fluctuations of the signals and the relatively short recording period. Besides these trends, some fluctuations can be detected in the corrected variations (anomalies) in Fig. 5. In the western region the rms amplitude of the residual component after removing the trend is about 0.2 C near the surface (0 100 m), 0.3 C at mid-depths (125 150 m), and 0.1 C at the deeper levels (200 400 m). At depths of 0 to 30 m, warm temperature anomalies are commonly found in 1986 87 and 1991 92, and cold anomalies are found in 1984 85 and 1993. At the deeper levels below 200 m, warm anomalies can be detected in 1986 88 and 1991 92, and this pattern is similar to that near the surface. After the removal of the 628 S. Aoki

linear trend, the residual temperature variations at 250 400 m have correlations with the surface temperature at correlation coefficients of 0.49 0.58 (which are significant around a 90% confidence level with 10 degrees of freedom). At the depths of the temperature minimum and the thermocline between 50 150 m, the variations are somewhat different from those at the shallower and deeper levels. Between 50 75 m, cold anomalies can be found in 1984, 1989, and 1993. Between 100 150 m, warm anomalies are found in 1985, 1988, and 1991. In the eastern region, there is an apparent warm anomaly in 1992 and cold anomaly in 1990 at depths of 0 to 30 m. Below 200 m, a warm anomaly was found in 1992 93 and cold anomaly in 1990 91. At levels of 50 125 m, as in the western region, the variations are different from those in shallower and deeper levels. Between 75 125 m there are warm anomalies in 1989 90 and 1992 and there is a cold anomaly in 1991. At around 100 m, the variations are similar to those in the same summer fall seasons at the same depths in the western region (although, there is a 3 month time lag between measurements in December and those in March in the next year). The correlation in between the western and eastern region is 0.64 and is significant above a confidence level of 90%. The OISST can be used as a reference to detect the real SST interannual variability. We compared the averaged JARE-SST and the OISST which are spatially and temporally combined with box averages (Fig. 8). The averages of the OISST were made over open ocean areas in each domain. The monthly averages were calculated for December in the western region and March in the eastern region. In the Fig. 8. Time series of the SST variations. Solid circles denote the JARE-SST, and squares denote the monthly averaged OISST. a) is for the western region and b) is for the eastern region. western region, warmer anomalies can be seen in 1984, 1987 and 1991 92 for the monthly averaged OISST. The averaged JARE-SST shows similar warm anomalies in 1987 and 1991 although a slightly warm anomaly is also found for 1989. In the eastern region the averaged OISST in March shows a warm anomaly in 1992 and cold anomalies in 1990 and 1994. The JARE-SST shows almost the same pattern, although the magnitude of its variation is slightly larger than that of the OISST. 4. Discussion and Conclusion Twelve years of the JARE XBT data are examined to study interannual temperature variations of the surface layer in the Indian sector of the Southern Ocean. One of the largest erroneous components is the difference in sampling positions. The WOA94 climatological data are used to reduce the errors caused by the different sampling positions. The reliability of the corrected variations is highly dependent on this approximation. The climatological field is not the exact mean field because the total observation period is different and the number of observations used is restricted. The discrepancy between the climatological field and actual mean field for the 12-year period cannot be calculated completely. Instead, the differences between the 12-year average for all observations and the corresponding average of the climatological profiles are calculated as a reference for the discrepancy. The magnitude of the difference between the JARE and WOA94 temperature field is different according to the depth. In the western region, the JARE temperatures are colder than the WOA94 fields by 0.1 0.5 C at depths of 0 50 m. The JARE temperatures are warmer by 0.3 0.4 C at around 150 m and by 0.1 0.2 C at 250 400 m. It was before the 1990s that the WOA94 data were mainly taken, thus the warm anomalies of the JARE observations may be consistent with the warming trend that was found in this study. However, cold anomalies near the surface cannot be explained. Further investigations are needed on the reliability of the climatological fields. Warming trends were found throughout depths of 0 400 m in the western region and, especially below 200 m, the trends are statistically significant. One of the largest error components is the effect of the different sampling points. However, below 200 m, the positive trend after the sampling correction is larger than that before. If the sampling correction is qualitatively valid, the correction cannot alter the sign of the trend (or even enhance the positive trend). To evaluate the robustness of these trends, random errors are added to the time series of the original observations and corrected variations, and the significance of the positive trends is examined. In total, 100 cases are examined for random errors with a certain amplitude, and the relation between the magnitude of the random errors and the significance of the fitted trend is studied. Derived estimates of the fitted trends can be thought to follow the Gaussian distribution. For a Interannual Variability in the Southern Ocean 629

depth of 200 m, a positive trend can be obtained above a 95% confidence level with a random error of 0.27 C standard deviation. For a depth of 400 m, that above a confidence level of 95% is obtained for the 0.19 C random errors. These error amplitudes are comparable to crude estimates of the total ambiguity. The errors in the sampling corrections are 0.1 0.2 C, which are represented by the difference between the JARE and WOA94 averages at these depths. The averaged measurement error in the XBT is about 0.03 C. If the contamination of the mesoscale variations is supposed to be 0.15 C, the total error will be 0.18 0.25 C. These results therefore indicate the warming signal is quite robust. Several other observations support the possibility that the warming tendency is a real oceanic signal. From historical data, a warming of 0.016 ± 0.009 C/year at the sea surface is reported in the latitude band of 50 60 S for 1958 77 (Ort et al., 1987). In recent years, warming trends of the deep and bottom waters of the Weddell Sea are found by continuous direct observations (Fahrbach et al., personal communication). For the southwest Pacific Ocean, a depth-averaged (0 4000 m) temperature rise of 0.04 C is observed at a latitude of 43 S, and 0.03 C at a latitude of 28 S from 1967 to 1989 90 (Bindoff and Church, 1992). Continuous and systematic observations will be fundamentally required to monitor this tendency. Signals of 4 5 year cycles can be traced to the depth of 400 m. The obtained variations show a similar pattern throughout most levels within 0 400 m. From 100 m to 400 m, the temperature increases with depth, so the positive anomaly at these depths corresponds to the shallowing of the isotherms. This can be consistent with the existence of a similar variation in SST and SSH and with the response due to the Ekman Pumping as in Jacobs and Mitchell (1996). Salinity data would be needed for any further discussion on quantitative congruity in the variations of the SSH and density structure. Note that the temperature variations at the temperature minimum layer (50 150 m) are different from those at shallower or deeper levels. This level corresponds to the Winter Water layer where winter sinking of surface water occurs, so this may be a reflection of some other surface phenomenon. Any potential relationship between the variations at the layer and the surface including sea ice states needs to be studied further. These results can be qualitatively reliable, but are not quantitatively concrete due to several erroneous components. To study these processes accurately, observations at virtually the same sampling points should be continuously conducted to eliminate the effect of the discussed positional factor and suppress contamination through mesoscale variations. The observation of variables such as salinity would be needed in further investigations relating to changes in water mass properties. Furthermore, future observations should be extended to much deeper levels in order to assess the global impact of these phenomena intelligently. Acknowledgements The author would like to express thanks to Mitsuo Fukuchi and Syuki Ushio for information pertaining to the JARE oceanographic observations. The author would also like to express gratitude to Tatsuo Motoi for information received regarding the Antarctic Circumpolar Wave. Discussion with Nathan Bindoff were quite invaluable. The author would also like to express thanks to Yutaka Michida and Syuhei Wakamatsu for providing the data. The thorough and helpful comments of the editor and reviewers on preliminary versions of the article are gratefully acknowledged. Brendan O Sullivan read the manuscript very carefully. Ayuko Ibaraki archived the data and helped in editing them. 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